| Title: | Build Network Based on Linear Mixed Models from EHRs |
| Version: | 1.0.0 |
| Description: | Analyzing longitudinal clinical data from Electronic Health Records (EHRs) using linear mixed models (LMM) and visualizing the results as networks. It includes functions for fitting LMM, normalizing adjacency matrices, and comparing networks. The package is designed for researchers in clinical and biomedical fields who need to model longitudinal data and explore relationships between variables For more details see Bates et al. (2015) <doi:10.18637/jss.v067.i01>. |
| License: | GPL-3 |
| Imports: | dplyr, lme4, qgraph |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Suggests: | knitr, rmarkdown, BiocStyle |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 3.5) |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2025-03-19 15:26:54 UTC; marina |
| Author: | Vargas-Fernández Marina [aut, cre], Martorell-Marugán Jordi [aut], Carmona-Sáez Pedro [aut] |
| Maintainer: | Vargas-Fernández Marina <marina.vargas@genyo.es> |
| Repository: | CRAN |
| Date/Publication: | 2025-03-21 16:00:06 UTC |
Subtract Two Adjacency Matrices
Description
This function returns difference matrix between two networks. It is useful for comparing two networks represented by adjacency matrices.
Usage
differentiation(matrix1, matrix2)
Arguments
matrix1 |
The first adjacency matrix. |
matrix2 |
The second adjacency matrix. |
Value
a normalized matrix containing values between 0 and 1.
Example Dataset: Psychological and Behavioral Responses
Description
This dataset contains self-reported psychological and behavioral responses from individuals.
Usage
example_data
Format
A data frame with multiple rows and 17 variables:
- id
Unique participant identifier (integer).
- Relax
Self-reported relaxation level (integer scale).
- Irritable
Self-reported irritability level (integer scale).
- Worry
Level of worry experienced (integer scale).
- Nervous
Self-reported nervousness (integer scale).
- Future
Concerns about the future (integer scale).
- Anhedonia
Self-reported lack of enjoyment (integer scale).
- Tired
Level of tiredness (integer scale).
- Hungry
Self-reported hunger level (integer scale).
- Alone
Feeling of loneliness (integer scale).
- Angry
Level of anger experienced (integer scale).
- Social_offline
Offline social interactions (integer scale).
- Social_online
Online social interactions (integer scale).
- Music
Time spent listening to music (integer scale).
- Procrastinate
Self-reported procrastination (integer scale).
- Outdoors
Time spent outdoors (integer scale).
- C19_occupied
Engagement in activities during COVID-19 (integer scale).
- C19_worry
Level of worry related to COVID-19 (integer scale).
- Home
Time spent at home (integer scale).
- day
Day number of the study (integer).
- beep
Moment within the day when data was collected (integer).
- conc
Self-reported concentration level (integer scale).
Details
This dataset was collected from a study examining psychological and behavioral responses to various daily experiences. Each row represents a unique moment of self-reporting.
Source
Reproducible figure for Nature Methods primer paper, Borsboom et al. 2021. This examples contains a subset of variables collected and modeled in our covid19 paper. This paper, with full data is available on https://journals.sagepub.com/doi/10.1177/21677026211017839. Eiko Fried, March 14 2021
Examples
data(example_data)
head(example_data)
Perform Longitudinal Analysis with Linear Mixed Models (LMM)
Description
This function automates the analysis of longitudinal clinical data using linear mixed models. It models clinical variables and returns a weighted matrix of model coefficient scores.
Usage
lmm_analysis(
clinical_data,
variables_to_scale,
random_effects = "(1 | participant_id)"
)
Arguments
clinical_data |
Dataframe containing clinical and metadata for participants, including identifier as |
variables_to_scale |
Character vector of variable names to be analyzed. |
random_effects |
A character string specifying the random effects formula (default: "(1 | participant_id)"). |
Value
A matrix of model coefficient scores, where rows represent dependent variables and columns represent independent variables.
Normalization of weighted linear mixed model network matrix.
Description
This function normalizes weighted adjacency matrix derived from lmm.
Usage
normalization(matrix)
Arguments
matrix |
The adjacency matrix (to be normalized). |
Value
a normalized matrix containing values between 0 and 1.
Compute Score Matrix
Description
This function adjusts an original matrix by copying the lower triangular part from a shifted matrix.
Usage
score_matrix(original_matrix, shifted_matrix)
Arguments
original_matrix |
A numeric matrix representing the original data. |
shifted_matrix |
A numeric matrix that has been transformed using |
Value
A new matrix with adjusted values in the lower triangular part.
Shifted Matrix Transformation
Description
This function modifies the shape of a model weights matrix by shifting its elements.
Usage
shift_matrix(mat)
Arguments
mat |
A numeric matrix to be transformed. |
Value
A shifted version of the input matrix.